Research on prediction of slurry migration distance in aggregate stacking based on GA-PSO-BPNN algorithm

After the water inrush accident in coal mine tunnels, early-stage pouring of aggregate forms a high-resistance, low-permeability aggregate stacking, transforming the pipeline flow into percolation. In the later stage, grouting is carried out into the interior of the aggregate stacking, effectively accumulating and solidifying the cement slurry. Among these, whether the slurry can migrate over long distances and fill the voids inside the aggregate stacking is the critical determinant of the success or failure of sealing. To quantitatively analyze the migration distance of slurry inside the aggregate stacking after grouting, a single-hole grouting test platform was established, and an orthogonal experiment was designed with grouting pressure, water cement ratio, and aggregate stacking porosity as influencing factors. Based on 25 sets of experimental measurements, four neural network prediction models suitable for studying the slurry migration distance within the aggregate stacking were constructed separately as back propagation neural network (BPNN), genetic algorithm (GA) combined BPNN, particle swarm optimization (PSO) combined BPNN, and GA-PSO combined BPNN. Evaluation criteria such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination (R2) were used for comparative analysis of the calculation errors and prediction accuracy of each model. From the perspective of neural network prediction results, the weight value of each influencing factor was analyzed, and the ranking was as follows: grouting pressure > aggregate particle size > water cement ratio, with grouting pressure being the primary controlling factor. The study demonstrates that the GA-PSO-BP model exhibits the best prediction performance, with an average relative error of only 1.59% and an R² of 0.998. This neural network model overcomes issues such as slow learning and getting stuck in tricky spots in BP neural networks. The prediction model shows high accuracy and stability, enabling more effective and accurate prediction of slurry migration distances, making it worthy of dissemination and application. This study can improve safety measures by reducing waste, expediting disaster management efforts, and minimizing environmental hazards associated with mining incidents

Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot

The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Development of Mathematical Models for the Assessment of Fire Risk of Some Indian Coals using Soft Computing Techniques

Coal is the dominant energy source in India and meets 56% of the country’s primary commercial energy supply. In the light of the realization of the supremacy of coal to meet the future energy demands, rapid mechanization of mines is taking place to augment the Indian coal production from 643.75 million tons (MT) per annum in 2014-15 to an expected level of 1086 MT per annum by 2024-25. Most of the coals in India are obtained from low-rank coal seams. Fires have been raging in several coal mines in Indian coalfields. Spontaneous heating of coal is a major problem in the global mining industry. Different researchers have reported that a majority (75%) of these fires owe their origin to spontaneous combustion of coal. Fires, whether surface or underground, pose serious and environmental problems are causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution on a massive scale. Over the years, the number of active mine fires in India has increased to an alarming 70 locations covering a cumulative area of 17 km2. In Indian coalfield, the fire has engulfed more than 50 million tons of prime coking coal, and about 200 million tons of coals are locked up due to fires. The seriousness of the problem has been realized by the Ministry of Coal, the Ministry of Labour, various statutory agencies and mining companies. The recommendations made in the 10th Conference on Safety in Mine held at New Delhi in 2007 as well as in the Indian Chamber of Commerce (ICC)-2006, New Delhi, it was stated that all the coal mining companies should rank their coal mines on a uniform scale according to their fire risk on scientific basis. This will help the mine planners/engineers to adopt precautionary measures/steps in advance against the occurrence and spread of coal mine fire. Most of the research work carried out in India focused on the assessment of spontaneous combustion liabilities of coals based on limited conventional experimental techniques. The investigators have proposed/established statistical models to establish correlation between various coal parameters, but limited work was done on the development of soft computing techniques to predict the propensity of coal to self-heating that is yet to get due attention. Also, the classifications that have been made earlier are based on limited works which were empirical in nature, without adequate and sound mathematical base. Keeping this in view, an attempt was made in this research work to study forty-nine coal samples of various ranks covering the majority of the Indian coalfields. The experimental/analytical methods that were used to assess the tendencies of coals to spontaneous heating were: proximate analysis, ultimate analysis, petrographic analysis, crossing point temperature, Olpinski index, flammability temperature, wet oxidation potential analysis and differential thermal analysis (DTA). The statistical regression analysis was carried out between the parameters of intrinsic properties and the susceptibility indices and the best-correlated parameters were used as inputs to the soft computing models. Further different ANN models such as Multilayer Perceptron Network (MLP), Functional Link Artificial Neural Network (FLANN) and Radial Basis Function (RBF) were applied for the assessment of fire risk potential of Indian coals. The proposed appropriate ANN fire risk prediction models were designed based on the best-correlated parameters (ultimate analysis) selected as inputs after rigorous statistical analysis. After the successful application of all the proposed ANN models, comparative studies were made based on Mean Magnitude of Relative Error (MMRE) as the performance parameter, model performance curves and Pearson residual boxplots. From the proposed ANN techniques, it was observed that Szb provided better fire risk prediction with RBF model vis-à-vis MLP and FLANN. The results of the proposed RBF network model was closely matching with the field records of the investigated Indian coals and can help the mine management to adopt appropriate strategies and effective action plans in advance to prevent occurrence and spread of fire

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer